Using the Natural Scenes’ Edges for Assessing Image Quality Blindly and Efficiently

Abstract

Two real blind/no-reference (NR) image quality assessment (IQA) algorithms in the spatial domain are developed. To measure image quality, the introduced approach uses an unprecedented concept for gathering a set of novel features based on edges of natural scenes. The enhanced sensitivity of the human eye to the information carried by edge and contour of an image supports this claim. The effectiveness of the proposed technique in quantifying image quality has been studied. The gathered features are formed using both Weibull distribution statistics and two sharpness functions to devise two separate NR IQA algorithms. The presented algorithms do not need training on databases of human judgments or even prior knowledge about expected distortions, so they are real NR IQA algorithms. In contrast to the most general no-reference IQA, the model used for this study is generic and has been created in such a way that it is not specified to any particular distortion type. When testing the proposed algorithms on LIVE database, experiments show that they correlate well with subjective opinion scores. They also show that the introduced methods significantly outperform the popular full-reference peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) methods. Besides they outperform the recently developed NR natural image quality evaluator (NIQE) model.

Three Different Features Based Metric To Assess Image Quality Blindly

“…For natural images (as the case in this study) the origin μ is usually close to zero, however, this parameter eliminated by stretching the contrast [21]. The extracted features is based on the hypothesis that the sharper and rich edge image the better is its quality [14,15,25]. The features obtained by (10) for image patches were fitted with MVG density (11), to give their rich representation [14].…”